Using Breast Cancer Risk Associated Polymorphisms to Identify Women for Breast Cancer Chemoprevention.

Elad Ziv,Karla Kerlikowske,Brian Sprague,Steven R Cummings,Jeffrey A Tice,Celine M Vachon

doi:10.1371/journal.pone.0168601

Abstract

BackgroundBreast cancer can be prevented with selective estrogen receptor modifiers (SERMs) and aromatase inhibitors (AIs). The US Preventive Services Task Force recommends that women with a 5-year breast cancer risk ≥3% consider chemoprevention for breast cancer. More than 70 single nucleotide polymorphisms (SNPs) have been associated with breast cancer. We sought to determine how to best integrate risk information from SNPs with other risk factors to risk stratify women for chemoprevention.MethodsWe used the risk distribution among women ages 35–69 estimated by the Breast Cancer Surveillance Consortium (BCSC) risk model. We modeled the effect of adding 70 SNPs to the BCSC model and examined how this would affect how many women are reclassified above and below the threshold for chemoprevention.ResultsWe found that most of the benefit of SNP testing a population is achieved by testing a modest fraction of the population. For example, if women with a 5-year BCSC risk of >2.0% are tested (~21% of all women), ~75% of the benefit of testing all women (shifting women above or below 3% 5-year risk) would be derived. If women with a 5-year risk of >1.5% are tested (~36% of all women), ~90% of the benefit of testing all women would be derived.ConclusionSNP testing is effective for reclassification of women for chemoprevention, but is unlikely to reclassify women with <1.5% 5-year risk. These results can be used to implement an efficient two-step testing approach to identify high risk women who may benefit from chemoprevention.

Highlights

Breast cancer risk can be reduced in women with the use of selective estrogen receptor modifiers (SERMs) such as tamoxifen[1] and raloxifene [2]or with the use of aromatase inhibitors (AIs)[3]
We found that most of the benefit of single nucleotide polymorphisms (SNPs) testing a population is achieved by testing a modest fraction of the population
Of the entire screened population, 6.9% had a 5-year risk of !3% according to the Breast Cancer Surveillance Consortium (BCSC) model prior to consideration of genetic risk, qualifying them for consideration for chemoprevention from breast cancer (Table 1)

Summary

Introduction

Breast cancer risk can be reduced in women with the use of selective estrogen receptor modifiers (SERMs) such as tamoxifen[1] and raloxifene [2]or with the use of aromatase inhibitors (AIs)[3]. The US Preventive Services Task Force (USPSTF) issued a recommendation that women at !3% 5 year risk of breast cancer consider preventive therapy for breast cancer[5]. A variety of models exist to risk stratify women for breast cancer including the Gail model[7], the Tyrer-Cuzick model[8] and the Breast Cancer Surveillance Consortium (BSCC) model[9] These models combine family history, reproductive risk factors, history of benign breast disease and mammographic density to risk stratify women. We sought to determine how to best integrate risk information from SNPs with other risk factors to risk stratify women for chemoprevention

Methods

Results

Conclusion